code: def xshard_to_np(shard, mode="fit", expand_dim=None):
      if mode == "fit":
          data_local = shard.collect()
          return (
              np.concatenate(
                  [data_local[i]["x"] for i in range(len(data_local))],
                  axis=0,
              ),
              np.concatenate(
                  [data_local[i]["y"] for i in range(len(data_local))],
                  axis=0,
              ),
          )
      if mode == "predict":
          data_local = shard.collect()
          return np.concatenate(
              [data_local[i]["x"] for i in range(len(data_local))],
              axis=0,
          )
      if mode == "yhat":
          yhat = shard.collect()
          yhat = np.concatenate(
              [yhat[i]["prediction"] for i in range(len(yhat))], axis=0
          )
          if len(expand_dim) >= 1:
              yhat = np.expand_dims(yhat, axis=expand_dim)
          return yhat
  
imports: import torch
  import torch.nn
  import warnings
  import random
  import numpy
  from torch.utils.data import TensorDataset, DataLoader
  import numpy
  import multiprocessing
  import warnings
  import math
label_desc: Use image node_id 4 for calling a global function with example usage: xshard_to_np(shard, mode, expand_dim) and returns: np,  np, np, yhat